How to evaluate product search quality
A practical framework for evaluating product-search results: representative shopper queries, evidence checks, provenance and deliberate failure tests.
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Author
Jay
Co-founder, simlir
Jay is the co-founder of simlir, working on product intelligence, product data, and search experiences for retailers and shopping assistants.
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How structured retailer data supports discovery in AI search
What AI search systems can read from public retailer pages, and how crawlability, structured data, internal links and stable identity support accurate discovery.
Product data quality checklist for retailers
A ten-part audit for retail catalogues: identifiers, variants, attributes, specifications, descriptions, images, price, availability, links and market scope.
From a product photo to structured candidates
Image product search is only useful when the result is more than a visual match. The stages from photo to structured candidates, and how to handle confidence and ambiguity.
Natural language product search compared with keyword search
Keyword search matches terms. Natural language search preserves the shopper
A practical product data schema for AI shopping assistants
Field by field: the identity, specification, media, review and price fields a product response needs for retrieval, comparison and explanation.
Why product prices need a source and checked time
A price without its currency, retailer and checked time cannot be explained. How to model price snapshots honestly, and what to do with stale or missing values.
MCP or REST for product data in a shopping assistant
MCP suits tool use inside an agent workflow. REST suits direct service integration. A decision table, auth notes and an implementation checklist for choosing between them.
How retailers can make product catalogues easier for shopping assistants to understand
The catalogue fields shopping assistants rely on: stable identifiers, comparable specs, price and availability context, images and canonical links. With a readiness checklist.
How to build product search for an AI shopping assistant
A seven-stage architecture for shopping assistant search: intent, retrieval, structured records, provenance, comparison, MCP or REST access, and failure testing.
What is a semantic product database?
A semantic product database retrieves products by meaning and intent, returning structured records an application can inspect, compare and explain.